




'''simple docstring'''


import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   OrderedDict(
    [
        ("align", "EfficientNetImageProcessor"),
        ("beit", "BeitImageProcessor"),
        ("bit", "BitImageProcessor"),
        ("blip", "BlipImageProcessor"),
        ("blip-2", "BlipImageProcessor"),
        ("bridgetower", "BridgeTowerImageProcessor"),
        ("chinese_clip", "ChineseCLIPImageProcessor"),
        ("clip", "CLIPImageProcessor"),
        ("clipseg", "ViTImageProcessor"),
        ("conditional_detr", "ConditionalDetrImageProcessor"),
        ("convnext", "ConvNextImageProcessor"),
        ("convnextv2", "ConvNextImageProcessor"),
        ("cvt", "ConvNextImageProcessor"),
        ("data2vec-vision", "BeitImageProcessor"),
        ("deformable_detr", "DeformableDetrImageProcessor"),
        ("deit", "DeiTImageProcessor"),
        ("deta", "DetaImageProcessor"),
        ("detr", "DetrImageProcessor"),
        ("dinat", "ViTImageProcessor"),
        ("donut-swin", "DonutImageProcessor"),
        ("dpt", "DPTImageProcessor"),
        ("efficientformer", "EfficientFormerImageProcessor"),
        ("efficientnet", "EfficientNetImageProcessor"),
        ("flava", "FlavaImageProcessor"),
        ("focalnet", "BitImageProcessor"),
        ("git", "CLIPImageProcessor"),
        ("glpn", "GLPNImageProcessor"),
        ("groupvit", "CLIPImageProcessor"),
        ("imagegpt", "ImageGPTImageProcessor"),
        ("instructblip", "BlipImageProcessor"),
        ("layoutlmv2", "LayoutLMv2ImageProcessor"),
        ("layoutlmv3", "LayoutLMv3ImageProcessor"),
        ("levit", "LevitImageProcessor"),
        ("mask2former", "Mask2FormerImageProcessor"),
        ("maskformer", "MaskFormerImageProcessor"),
        ("mgp-str", "ViTImageProcessor"),
        ("mobilenet_v1", "MobileNetV1ImageProcessor"),
        ("mobilenet_v2", "MobileNetV2ImageProcessor"),
        ("mobilevit", "MobileViTImageProcessor"),
        ("mobilevit", "MobileViTImageProcessor"),
        ("mobilevitv2", "MobileViTImageProcessor"),
        ("nat", "ViTImageProcessor"),
        ("oneformer", "OneFormerImageProcessor"),
        ("owlvit", "OwlViTImageProcessor"),
        ("perceiver", "PerceiverImageProcessor"),
        ("pix2struct", "Pix2StructImageProcessor"),
        ("poolformer", "PoolFormerImageProcessor"),
        ("regnet", "ConvNextImageProcessor"),
        ("resnet", "ConvNextImageProcessor"),
        ("sam", "SamImageProcessor"),
        ("segformer", "SegformerImageProcessor"),
        ("swiftformer", "ViTImageProcessor"),
        ("swin", "ViTImageProcessor"),
        ("swin2sr", "Swin2SRImageProcessor"),
        ("swinv2", "ViTImageProcessor"),
        ("table-transformer", "DetrImageProcessor"),
        ("timesformer", "VideoMAEImageProcessor"),
        ("tvlt", "TvltImageProcessor"),
        ("upernet", "SegformerImageProcessor"),
        ("van", "ConvNextImageProcessor"),
        ("videomae", "VideoMAEImageProcessor"),
        ("vilt", "ViltImageProcessor"),
        ("vit", "ViTImageProcessor"),
        ("vit_hybrid", "ViTHybridImageProcessor"),
        ("vit_mae", "ViTImageProcessor"),
        ("vit_msn", "ViTImageProcessor"),
        ("xclip", "CLIPImageProcessor"),
        ("yolos", "YolosImageProcessor"),
    ]
)

UpperCAmelCase_					=   _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)


def     SCREAMING_SNAKE_CASE							(   a_						:				str      ):
  for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
    if class_name in extractors:
      __a								=       model_type_to_module_name(a_      )

      __a								=       importlib.import_module(f".{module_name}"					,       'transformers.models'      )
      try:
        return getattr(a_					,       a_      )
      except AttributeError:
        continue

  for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
    if getattr(a_					,       '__name__'					,       a_      ) == class_name:
      return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
  __a								=       importlib.import_module('transformers'      )
  if hasattr(a_					,       a_      ):
    return getattr(a_					,       a_      )

  return None


def     SCREAMING_SNAKE_CASE							(   a_						:				Union[str, os.PathLike]					,       a_						:				Optional[Union[str, os.PathLike]] = None					,       a_						:				bool = False					,       a_						:				bool = False					,       a_						:				Optional[Dict[str, str]] = None					,       a_						:				Optional[Union[bool, str]] = None					,       a_						:				Optional[str] = None					,       a_						:				bool = False					,       **a_						:				Optional[int]					,       ):
  __a								=       get_file_from_repo(
      a_					,       a_					,       cache_dir=a_					,       force_download=a_					,       resume_download=a_					,       proxies=a_					,       use_auth_token=a_					,       revision=a_					,       local_files_only=a_					,       )
  if resolved_config_file is None:
    logger.info(
        'Could not locate the image processor configuration file, will try to use the model config instead.'      )
    return {}

  with open(a_					,       encoding='utf-8'      ) as reader:
    return json.load(a_      )





class  __lowercase    :







   def __init__(						self   )					->     int:
     raise EnvironmentError(
         'AutoImageProcessor is designed to be instantiated '
         'using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.'   )







   @classmethod
   @replace_list_option_in_docstrings(UpperCamelCase   )
   def 		UpperCamelCase__      (						cls   ,				UpperCamelCase   ,				**UpperCamelCase   )					->     List[Any]:
     __a								=       kwargs.pop('config'   ,				UpperCamelCase   )
     __a								=       kwargs.pop('trust_remote_code'   ,				UpperCamelCase   )
     __a								=       True

     __a						,							__a								=       ImageProcessingMixin.get_image_processor_dict(UpperCamelCase   ,				**UpperCamelCase   )
     __a								=       config_dict.get('image_processor_type'   ,				UpperCamelCase   )
     __a								=       None
     if "AutoImageProcessor" in config_dict.get('auto_map'   ,				{}   ):
       __a								=       config_dict['auto_map']['AutoImageProcessor']

     # If we still don't have the image processor class, check if we're loading from a previous feature extractor config
     # and if so, infer the image processor class from there.
     if image_processor_class is None and image_processor_auto_map is None:
       __a								=       config_dict.pop('feature_extractor_type'   ,				UpperCamelCase   )
       if feature_extractor_class is not None:
         logger.warning(
             'Could not find image processor class in the image processor config or the model config. Loading'
             ' based on pattern matching with the model\'s feature extractor configuration.'   )
         __a								=       feature_extractor_class.replace('FeatureExtractor'   ,				'ImageProcessor'   )
       if "AutoFeatureExtractor" in config_dict.get('auto_map'   ,				{}   ):
         __a								=       config_dict['auto_map']['AutoFeatureExtractor']
         __a								=       feature_extractor_auto_map.replace('FeatureExtractor'   ,				'ImageProcessor'   )
         logger.warning(
             'Could not find image processor auto map in the image processor config or the model config.'
             ' Loading based on pattern matching with the model\'s feature extractor configuration.'   )

        # If we don't find the image processor class in the image processor config, let's try the model config.
     if image_processor_class is None and image_processor_auto_map is None:
       if not isinstance(UpperCamelCase   ,				UpperCamelCase   ):
         __a								=       AutoConfig.from_pretrained(UpperCamelCase   ,				**UpperCamelCase   )
       # It could be in `config.image_processor_type``
       __a								=       getattr(UpperCamelCase   ,				'image_processor_type'   ,				UpperCamelCase   )
       if hasattr(UpperCamelCase   ,				'auto_map'   ) and "AutoImageProcessor" in config.auto_map:
         __a								=       config.auto_map['AutoImageProcessor']

     if image_processor_class is not None:
       __a								=       image_processor_class_from_name(UpperCamelCase   )

     __a								=       image_processor_auto_map is not None
     __a								=       image_processor_class is not None or type(UpperCamelCase   ) in IMAGE_PROCESSOR_MAPPING
     __a								=       resolve_trust_remote_code(
         UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )

     if has_remote_code and trust_remote_code:
       __a								=       get_class_from_dynamic_module(
           UpperCamelCase   ,				UpperCamelCase   ,				**UpperCamelCase   )
       __a								=       kwargs.pop('code_revision'   ,				UpperCamelCase   )
       if os.path.isdir(UpperCamelCase   ):
         image_processor_class.register_for_auto_class()
       return image_processor_class.from_dict(UpperCamelCase   ,				**UpperCamelCase   )
     elif image_processor_class is not None:
       return image_processor_class.from_dict(UpperCamelCase   ,				**UpperCamelCase   )
     # Last try: we use the IMAGE_PROCESSOR_MAPPING.
     elif type(UpperCamelCase   ) in IMAGE_PROCESSOR_MAPPING:
       __a								=       IMAGE_PROCESSOR_MAPPING[type(UpperCamelCase   )]
       return image_processor_class.from_dict(UpperCamelCase   ,				**UpperCamelCase   )

     raise ValueError(
         f"Unrecognized image processor in {pretrained_model_name_or_path}. Should have a "
         f"`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following "
         f"`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()   )}"   )







   @staticmethod
   def 		UpperCamelCase__      (						UpperCamelCase   ,				UpperCamelCase   )					->     Optional[int]:
     IMAGE_PROCESSOR_MAPPING.register(UpperCamelCase   ,				UpperCamelCase   )









'''simple docstring'''


import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment





@require_huggingface_suite
class  __lowercase    (	unittest.TestCase			):







   def 		UpperCamelCase__      (						self   )					->     Tuple:
     __a								=       inspect.getfile(accelerate.test_utils   )
     __a								=       os.path.sep.join(
         mod_file.split(os.path.sep   )[:-1] + ['scripts', 'external_deps', 'test_metrics.py']   )

     from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

     __a								=       test_metrics







   @require_cpu
   def 		UpperCamelCase__      (						self   )					->     Tuple:
     debug_launcher(self.test_metrics.main   ,				num_processes=1   )







   @require_cpu
   def 		UpperCamelCase__      (						self   )					->     str:
     debug_launcher(self.test_metrics.main   )







   @require_single_gpu
   def 		UpperCamelCase__      (						self   )					->     Dict:
     self.test_metrics.main()







   @require_multi_gpu
   def 		UpperCamelCase__      (						self   )					->     int:
     print(f"Found {torch.cuda.device_count()} devices."   )
     __a								=       ['torchrun', f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
     with patch_environment(omp_num_threads=1   ):
       execute_subprocess_async(UpperCamelCase   ,				env=os.environ.copy()   )



